Abstract Project 3 of the Sedentary Time and Aging Research (STAR) Program will investigate how sitting time, standing time, sit-to-stand transitions and physical activity time are related to biomarkers of healthy aging, physical functioning and 5-7 year mortality risk. This Project will apply novel, accurate, machine-learned, behavior-based classification methods to define new SB variables using existing accelerometer data. These SB behaviors will be studied in relation to healthy aging biomarkers (cross-sectionally), and mortality and changes in physical function through 2020 in a large (>6000), diverse (50% minority) cohort of postmenopausal women from the Women's Health Initiative (WHI) who wore accelerometers in 2012- 2014 as part of the ?Objective Physical Activity and Cardiovascular Health? (OPACH) ancillary study (PI: A. LaCroix). There is growing evidence in large cohort studies for a relationship between sedentary behavior and health, adjusting for physical activity. Further, these studies have shown that older adults are most at risk for inactivity. Many studies have been cross-sectional and most have employed self-reported measures of sitting time and focused on a limited range of outcomes. Few studies have focused on prospective relationships with physical functioning and maintaining mobility, which are highly salient outcomes for preserving independence in older adults. When studies have included objective accelerometer measures of sedentary time and physical activity, they have employed intensity-based cutpoints. Our research shows that these cutpoints misclassify behaviors including sitting in vehicle, standing and walking. In Project 3, we propose to accurately classify specific behaviors being studied in Projects 1 and 2, including sitting time, standing time, sit-to-stand transitions, and walking/running by employing new computational techniques with the existing accelerometer data. We will employ machine learned classifiers (that have been trained on data from older women and perform against known truths with 85% accuracy) to assess total minutes in each behavior. In addition, we will refine our existing classifiers for sit-to-stand transitions by observing transitions in Project 1 and validating the new algorithms against thigh worn inclinometer data in Project 2. The algorithms will then be applied to the existing accelerometer data in Project 3. Project 3 will assess cross-sectional relationships with existing biomarkers of glucose regulation, endothelial functioning (inflammation and blood pressure) and lipids. Mortality and self-reported physical functioning will be assessed annually through 2020. Models will explore the strength, shape and independence of relationships cross-sectionally and over time. The Biostatistics Core will build upon these analyses by exploring prolonged bouts of sitting, and the time of day when bouts occur. We will map these temporal patterns to Project 2 outcomes. Project 3 will provide valuable new evidence to inform public health guidelines on how best to interrupt sitting to improve prospects for healthy aging.